Using workload profiling and analytics to understand and score complexity of test environments and workloads

ABSTRACT

Aspects of the present invention include a method, system and computer program product determining, scoring and reporting the complexity of customer and test environments and workloads. The method includes a processor performing an accounting of factors related to complexity of a plurality of environments and workloads; determining one or more formulas to use for determining an overall score and ranking for each one of the plurality of environments and workloads; collecting relative environment and workload data; determining a complexity score for each one of the plurality of environments and workloads; and determining a complexity ranking for each one of the plurality of environments and workloads.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patent application Ser. No. 15/259,130 filed on Sep. 8, 2016, the contents of which are incorporated herein by reference.

BACKGROUND

The present invention relates to the testing of software, hardware, firmware, and/or other disciplines, and more specifically, to a method, system and computer program product that implement aspects of workload and operational profiling, coupled with business analytics, thereby resulting in improvements in the testing of customer software.

In the field of software testing, as in many other technical fields, improvements are constantly being sought, primarily for cost and accuracy reasons. A fundamental goal of software testing, in theory, is to identify all of the problems in a customer's software program before the program is released for use by the customer. However, in reality, this is far from the case as typically a software program is released to the customer having some number of problems that were unidentified during the software development and testing process.

A relatively more proactive approach to improving software testing is sought that employs traditional methods of understanding characteristics of clients' environments, augmented with a process of data mining empirical systems data. Such client environment and workload profiling analysis may result in software test improvements based on characteristics comparisons between the client and the test environments.

SUMMARY

According to one or more embodiments of the present invention, a computer-implemented method includes performing, by a processor, an accounting of factors related to complexity of a plurality of environments and workloads; determining, by the processor, one or more formulas to use for determining an overall score and ranking for each one of the plurality of environments and workloads; collecting, by the processor, relative environment and workload data; determining, by the processor, a complexity score for each one of the plurality of environments and workloads; and determining, by the processor, a complexity ranking for each one of the plurality of environments and workloads.

According to another embodiment of the present invention, a system includes a processor in communication with one or more types of memory, the processor configured to perform an accounting of factors related to complexity of a plurality of environments and workloads; to determine one or more formulas to use for determining an overall score and ranking for each one of the plurality of environments and workloads; to collect relative environment and workload data; to determine a complexity score for each one of the plurality of environments and workloads; and to determine a complexity ranking for each one of the plurality of environments and workloads.

According to yet another embodiment of the present invention, a computer program product includes a non-transitory storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method that includes performing an accounting of factors related to complexity of a plurality of environments and workloads; determining one or more formulas to use for determining an overall score and ranking for each one of the plurality of environments and workloads; collecting relative environment and workload data; determining a complexity score for each one of the plurality of environments and workloads; and determining a complexity ranking for each one of the plurality of environments and workloads.

Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention. For a better understanding of the invention with the advantages and the features, refer to the description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The forgoing and other features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing environment according to one or more embodiments of the present invention;

FIG. 2 depicts abstraction model layers according to one or more embodiments of the present invention;

FIG. 3 is a block diagram illustrating one example of a processing system for practice of the teachings herein;

FIG. 4 is a flow diagram of a method for determining, scoring and reporting the complexity of customer and test environments and workloads in accordance with one or more embodiments of the present invention.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.

Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.

Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 1 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers provided by cloud computing environment 50 (FIG. 1) is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 2 are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.

In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and a method 96 for determining, scoring and reporting the complexity of customer and test environments and workloads in accordance with one or more embodiments of the present invention.

Referring to FIG. 3, there is shown a processing system 100 for implementing the teachings herein according to one or more embodiments. The system 100 has one or more central processing units (processors) 101 a, 101 b, 101 c, etc. (collectively or generically referred to as processor(s) 101). In one embodiment, each processor 101 may include a reduced instruction set computer (RISC) microprocessor. Processors 101 are coupled to system memory 114 and various other components via a system bus 113. Read only memory (ROM) 102 is coupled to the system bus 113 and may include a basic input/output system (BIOS), which controls certain basic functions of system 100.

FIG. 3 further depicts an input/output (I/O) adapter 107 and a network adapter 106 coupled to the system bus 113. I/O adapter 107 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 103 and/or tape storage drive 105 or any other similar component. I/O adapter 107, hard disk 103, and tape storage device 105 are collectively referred to herein as mass storage 104. Operating system 120 for execution on the processing system 100 may be stored in mass storage 104. A network adapter 106 interconnects bus 113 with an outside network 116 enabling data processing system 100 to communicate with other such systems. A screen (e.g., a display monitor) 115 is connected to system bus 113 by display adaptor 112, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one embodiment, adapters 107, 106, and 112 may be connected to one or more I/O busses that are connected to system bus 113 via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system bus 113 via user interface adapter 108 and display adapter 112. A keyboard 109, mouse 110, and speaker 111 all interconnected to bus 113 via user interface adapter 108, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.

In exemplary embodiments, the processing system 100 includes a graphics processing unit 130. Graphics processing unit 130 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit 130 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

Thus, as configured in FIG. 3, the system 100 includes processing capability in the form of processors 101, storage capability including system memory 114 and mass storage 104, input means such as keyboard 109 and mouse 110, and output capability including speaker 111 and display 115. In one embodiment, a portion of system memory 114 and mass storage 104 collectively store an operating system to coordinate the functions of the various components shown in FIG. 3.

In accordance with one or more embodiments of the present invention, methods, systems, and computer program products are disclosed for determining, scoring and reporting the complexity of customer and test environments and workloads.

In one or more embodiments of the present invention, the method 200 may be embodied in software that is executed by computer elements located within a network that may reside in the cloud, such as the cloud computing environment 50 described hereinabove and illustrated in FIGS. 1 and 2. In other embodiments, the computer elements may reside on a computer system or processing system, such as the processing system 100 described hereinabove and illustrated in FIG. 3, or in some other type of computing or processing environment.

After a start operation in block 204, an operation in block 208 takes or performs an accounting of a number of various environment and workload complexity factors and characteristics, individually and/or collectively in various groupings, to determine or calculate various metrics including component level and an overall complexity score or grade and rankings (e.g., each workload's complexity scores, by applicable sub-area and overall score). The determined or calculated metrics may be stored in a database.

These environment and workload complexity factors and characteristics may include, for example and without limitation, environment configurations, including: server; storage; network; Sysplex, LPAR, Adapter, etc.; HMC; other IBM hardware platforms/products; dedicated vs. shared resources; physical vs. virtual resources; z/OS, zVM, zLinux, etc.; middleware including CICS, DB2, IMS, MQ, WAS, and others; other IBM software platforms/products; Cloud (Compute, Storage, Network); and OEM hardware and software platforms and products. They may also include: functional coverage; scalability reliability; availability; serviceability; error recoverability; upgrades and migrations; performance indicators including response times, transaction rates, etc.; products; product combinations, interaction, dependencies; cross platform product combinations, interactions and dependencies; systems; problem discovery/defect identification; PMR generation and severities; APAR generation and severities; skills requirements; additional structured and unstructured workload internals information as provided by customer subject matter expert(s); and additional structured and unstructured workload internals information as provided by IBM Design, Development, Performance, Support, and/or Test Subject Matter Expert(s).

In an operation in block 212, the necessary formulas are determined to properly score and rank these complexity characteristics and requirements. This may be done through consultation with subject matter experts from each environment and workload to be analyzed for complexity implementation, scoring and ranking. The subject matter experts may provide information, recommendations and/or guidelines. The formulas may be stored in a database as a preparation or prerequisite for analytics processing.

In an operation in block 216, acquire or collect the relative environment and workload data using the current and continually expanding customer profiling and analytics discipline techniques for data collection and curation. These techniques include, for example and without limitation, environment and workload and operational questionnaires, interviews, workshops, deep dives, problem history analysis, social and traditional media analysis, empirical data analysis, and more. This environment and workload data may be stored in a database as a preparation or prerequisite for analytics processing.

In an operation in block 220, the complexity scores for each environment and workload to be analyzed are determined or calculated using, for example, the formulas determined in the operation in block 212. These complexity scores may be stored in a database.

An operation in block 224 determines or calculates the complexity ranking of each environment and workload to be analyzed in relation to various previously collected customer and/or test environment and workload data and through a variety of customer and/or test groupings. These customer and/or test groupings can include a variety of classifications, including by geography, country, culture, industry, etc. as well as the overall global customer and/or test set. These complexity rankings may be stored in a database.

Next, a decision operation in block 228 determines whether or not to visualize the complexity factors and implementation of this iteration of the method 200. If so, in an operation in block 232, the complexity environment and workload factors and characteristics (including implementation, data/observations, formulas, scores, rankings, and other relative information) are visually presented in a highly intuitive, customizable, negotiable, descriptive, and flexible dashboard type interface and/or through other desired visualizations.

Next, a decision operation in block 236 determines whether or not to generate report(s) for the complexity factors and characteristics of this iteration of the method 200. If not, the method 200 branches back to the operation in block 208. If so, in an operation in block 240, the complexity environment and workload factors and characteristics (including implementation, data/observations, formulas, scores, rankings, and other relative information) are presented in a highly intuitive, customizable, negotiable, descriptive, and flexible canned and/or end user designed and generated report(s). Then store these complexity factors report specifications and data in a database, for among other purposes, for current/future statistical analyses and for current/future time series observations and analyses. The method 200 then branches back to the operation in block 208 in which the method iterates the customer profiling and analytics of complexity factors and characteristics as changes are made to the environment and/or workload.

Given that Test workload runs have different levels of complexity, and can be resource and time intensive, limited in availability, and financially expensive to configure, stage, run, and analyze, and can span multiple days or even weeks (including non-user monitored off-shift and weekend time), providing a run time and historical way to assess and score customer and test workload complexity can be potentially relatively cost effective.

According to embodiments of the present invention, the run time and historical workload complexity report scoring provides multiple capabilities, efficiencies, and financial benefits for the test user or operator and/or customer including: to understand the run time complexity of the customer workload (software, hardware, firmware) and what corrective test workload run time adjustments may be required to achieve (if possible); to tune test workloads much closer to their target complexity goal(s) through the very nature of faster, run time notification and awareness, as well as insightful, analytics driven historical reference; to significantly reduce the amount of limited and high value System z systems, storage, network, environmental, personnel time and resources to accomplish test objectives, resulting in both financial savings and reduced environmental impact; to increase test plan efficiency through expanded test coverage, resulting in enhanced product quality and greater customer satisfaction. By the reduction of repeat test workload runs through higher individual workload run complexity effectiveness, the test user/operator can run additional and/or expanded test cases/scenarios, and ensure each workload run maximizes a successful outcome; and to provide insights to the customer on potential workload complexity reductions and/or efficiencies (i.e., possibly determine ways to simplify the customer workload should there be opportunities to do so). These potential customer workload complexity reductions/efficiencies could result in a wide range of customer benefits including financial and skills savings, reduced problems/outages, faster problem determination and resolution, enhanced performance, and more.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.

As used herein, the articles “a” and “an” preceding an element or component are intended to be nonrestrictive regarding the number of instances (i.e., occurrences) of the element or component. Therefore, “a” or “an” should be read to include one or at least one, and the singular word form of the element or component also includes the plural unless the number is obviously meant to be singular.

As used herein, the terms “invention” or “present invention” are non-limiting terms and not intended to refer to any single aspect of the particular invention but encompass all possible aspects as described in the specification and the claims.

As used herein, the term “about” modifying the quantity of an ingredient, component, or reactant of the invention employed refers to variation in the numerical quantity that can occur, for example, through typical measuring and liquid handling procedures used for making concentrates or solutions. Furthermore, variation can occur from inadvertent error in measuring procedures, differences in the manufacture, source, or purity of the ingredients employed to make the compositions or carry out the methods, and the like. In one aspect, the term “about” means within 10% of the reported numerical value. In another aspect, the term “about” means within 5% of the reported numerical value. Yet, in another aspect, the term “about” means within 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1% of the reported numerical value.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 

What is claimed is:
 1. A computer-implemented method comprising: performing, by a processor, an accounting of factors related to complexity of a plurality of environments and workloads; determining, by the processor, one or more formulas to use for determining an overall score and ranking for each one of the plurality of environments and workloads; collecting, by the processor, relative environment and workload data; determining, by the processor, a complexity score for each one of the plurality of environments and workloads; and determining, by the processor, a complexity ranking for each one of the plurality of environment and workloads.
 2. The computer-implemented method of claim 1 further comprising determining, by the processor, to present in visual format the complexity score and the complexity ranking.
 3. The computer-implemented method of claim 1 further comprising determining, by the processor, to present in report format the complexity score and the complexity ranking.
 4. The computer-implemented method of claim 1 wherein performing, by a processor, an accounting of factors related to complexity of a plurality of environments and workload s comprises performing, by the processor, an accounting of factors related to complexity of a plurality of environments and workloads individually and/or collectively in various groupings.
 5. The computer-implemented method of claim 1 wherein the factors related to complexity of a plurality of environments and workloads comprise environment configurations.
 6. The computer-implemented method of claim 1 wherein collecting, by the processor, relative environment and workload data comprises techniques including environment and workload and operational questionnaires, interviews, workshops, deep dives, problem history analysis, social and traditional media analysis, empirical data analysis.
 7. The computer-implemented method of claim 1 wherein determining, by the processor, a complexity ranking for each one of the plurality of environment and workloads comprises determining, by the processor, a complexity ranking of each environment and workload to be analyzed in relation to various previously collected customer and/or test environment and workload data and through a variety of customer and/or test groupings including a variety of classifications, including by geography, country, culture, industry, an overall global customer set and a test set. 